Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery
Notice bibliographique
Résumé
Abstract Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional histopathology examination (gold standard) to assess margin status (positive or negative) in patients undergoing surgery for BCa. A multivariate model was trained using pathology-validated mass spectra from ex vivo resection specimens from 11 patients, including 36 spectra from invasive cancer, and 118 spectra from normal breast adipose. This model was subsequently tested retrospectively on intraoperative data from 25 BCa cases that were conducted using navigated REIMS. Navigation was facilitated by an electromagnetic sensor placed on the cautery. To record the position of the tumor, a localization wire fitted with an electromagnetic sensor was placed into the tumor using ultrasound prior to surgery, and a 3D map of the tumor was created. This enabled real-time knowledge of the cautery position relative to the tumor during surgery. Spectra from the plume classified as BCa were mapped onto a display of the tumor region and compared with the pathology report. Our multivariate classifier exhibited >90% accuracy on cross-validation, driven by an elevated ratio of glycerophospholipid:triglyceride in cancer as compared with normal adipose. In the intraoperative testing cohort, 4/150 margins were assessed by pathology as positive, all of which were correctly identified by REIMS (Sensitivity=100%). Notably, two of these cases were positive for ductal carcinoma in situ. 146/150 margins were negative, of which 131/146 negative margins were consistent with histopathology (Specificity=90%). There were 13 negative margins that were determined to be positive by REIMS (false positive), of which 8 margins were noted as either ‘close margins’ (with cancer cells being detected within 1 mm of the inked margin on histopathology), or as high-density normal breast tissue. Other normal tissue such as skin and muscle also exhibited high phospholipid content but these spectra could be rationalized by relative distance from the tumor region using the navigation data and/or information provided by the surgeon in real-time. We have demonstrated the importance of spatio-temporal tracking and histopathology to validate intraoperative REIMS. Furthermore, we have shown the feasibility of navigated REIMS in identifying margins containing cancer in real-time during BCa surgery, where a wider excision may be desirable. Citation Format: Martin Kaufmann, Amoon Jamzad, Tamas Ungi, Jessica R Rodgers, Teaghan Koster, Chris Yeung, Josh Ehrlich, Alice Santilli, Mark Asselin, Natasja Janssen, Julie McMullen, Kathryn Solberg, Joanna Cheesman, Alessia Di Carlo, Kevin Yi Mi Ren, Sonal Varma, Shaila Merchant, Cecil Jay Engel, G Ross Walker, Andrea Gallo, Doris Jabs, Parvin Mousavi, Gabor Fichtinger, John F Rudan. Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-23-07.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».